Keebler Health Gets $16M to Read Healthcare’s Hidden Clinical Stories

DURHAM, N.C. – April 15, 2026 – Keebler Health, a startup building its technology on the foundation of large language models (LLMs), has secured $16 million in a Series A funding round to tackle one of healthcare’s most persistent and costly challenges: making sense of unstructured clinical data. The round, led by prominent healthcare investor Flare Capital Partners with participation from Sands Capital, brings the company's total funding to $23 million since its founding in 2023.

The investment signals strong investor confidence in Keebler's approach to revolutionizing risk adjustment, a critical process for organizations operating under value-based care models. By deploying advanced AI to read and interpret the vast narratives hidden in physician notes, discharge summaries, and lab reports, the company aims to create a complete and accurate picture of patient health, which has direct implications for both financial reimbursement and clinical outcomes.

“Risk adjustment doesn’t just have an awareness problem, it has an approach problem,” said Isaac Park, CEO and co-founder of Keebler Health, in a statement. “At its core, capturing the patient’s medical story with complete accuracy comes from information that lives in the provider notes, where care is documented. We built Keebler to bring that medical story forward so decisions about risk and reimbursement reflect the holistic view of each patient's health.”

The 80% Problem: Cracking Unstructured Data

The core challenge Keebler Health is tackling is often referred to as healthcare’s “80% problem.” Industry estimates suggest that approximately 80% of all medical information is unstructured, meaning it exists as free-form text, scanned documents, or images rather than in neatly organized, coded fields within an Electronic Health Record (EHR). This includes the detailed narratives where clinicians document a patient’s history, symptoms, and care journey.

This data fragmentation has profound consequences. A study published in the Journal of the American Medical Informatics Association found that only 59.4% of chronic conditions are consistently captured across different EHR data sources. For value-based care organizations, whose reimbursement is tied to the documented health risk of their patient populations, these gaps translate directly into inaccurate risk scores and lost revenue. More importantly, an incomplete patient record can lead to missed opportunities for proactive care and intervention.

Traditional methods to bridge this gap have relied on manual chart reviews—a costly, time-consuming, and error-prone process—or older Natural Language Processing (NLP) technologies. These legacy NLP systems often use rule-based or statistical models that struggle with the nuance, abbreviations, and contextual complexity of clinical language, limiting their effectiveness and scalability.

An LLM-Native Advantage in a Crowded Field

Keebler Health asserts that its platform, built from the ground up on LLMs, represents a fundamental shift. Unlike systems where AI is retrofitted onto existing workflows, its LLM-native architecture is designed to process the full clinical narrative with a deeper semantic understanding. This allows it to identify and surface missed Hierarchical Condition Category (HCC) coding opportunities with greater accuracy across both retrospective and concurrent workflows.

This technological differentiation was a key factor for its investors. “What stood out to us about Keebler is how clearly the team is executing against a long-standing limitation in healthcare data,” commented Ian Chiang, Partner at Flare Capital Partners. “They’ve built a platform that aligns with how clinical information is actually documented and are already demonstrating the ability to turn that into meaningful, measurable results.”

The market for AI in healthcare is booming, with projections showing the global healthcare LLM platform market could skyrocket from just over $1 billion in 2024 to over $22 billion by 2033. Keebler enters a competitive landscape populated by established players like Apixio and Cotiviti, as well as other innovators like RAAPID, all vying to help providers and payers optimize risk adjustment. Keebler's focus on being LLM-native and processing the entire patient chart—including complex sources like handwritten notes and scanned PDFs—is its intended edge.

Christy Steele, Partner at Sands Capital, echoed this sentiment, stating, “Keebler is addressing a system-level constraint in how patient documentation is used across healthcare, with a solution that is both technically differentiated and practical to deploy.”

From Financial ROI to Better Patient Care

While the immediate value proposition is financial, the company’s vision extends to fundamentally improving patient care. By providing clinicians with actionable insights at the point of care, the platform aims to support better clinical decision-making without disrupting established workflows. The company claims its technology can identify 15-30% more disease burden than traditional methods.

Early results reported by the company are compelling. In one pilot, a provider group using the platform on just 44 complex patients uncovered 98 previously undocumented conditions, leading to significantly improved risk scores. Another customer reportedly achieved a 75x return on investment in its first year and cut its risk adjustment infrastructure costs by 70%, from $2 million to $600,000 annually. This efficiency transforms a months-long retrospective review process into a real-time analytical capability.

The new funding will fuel Keebler Health's commercial expansion and team growth, but it is also earmarked for developing adjacent capabilities. With increasing regulatory scrutiny on risk adjustment, the company plans to expand into compliance and audit workflows, offering AI-enabled RADV audit readiness. This long-term strategy involves leveraging its powerful data-reading infrastructure to support population health initiatives, such as identifying the Next Best Diagnostic Action for patients and enabling proactive risk management across an entire health system.

As AI continues to integrate into clinical practice, navigating regulatory frameworks like HIPAA and addressing ethical concerns such as algorithmic bias will be paramount. By building a platform that provides auditable, evidence-backed recommendations, Keebler Health aims to build the trust necessary for widespread adoption and realize its goal of unlocking the full patient story for a healthier future.